Business intelligence systems are used in substantially all industries to gather, store, analyze, and report on business data or business intelligence. For example, manufacturing facilities use business intelligence systems to evaluate and report on factory production and personnel productivity as well as other critical metrics. Retailers use business intelligence systems to analyze trends in sales and to prepare reports for upper management.
Traditional business intelligence systems are not well-defined, predictable, or testable. For example, using traditional business intelligence systems to develop a report specification might be done as follows.
First, a business consumer requests a new report specification, and the task of creating the report specification is assigned to a report author skilled in the use of the report-authoring tool provided by the business intelligence software suite. While creating this report the report author might, for example, discover that the metadata model contained within the business intelligence software suite does not provide the information required for the report. The report author would in that case request that a change be made to the metadata model, and finish developing the report specification after the change is made.
Once the report author finishes the report specification it is sent to a quality inspector or directly to the business customer for testing. Whoever is testing the report must manually execute the report, visually inspect the report for defects, make notes about any defects found, and generate an error report that is returned to the report author for correction. The report author will attempt to make corrections to the report according to the new requirements provided in the notes. Once the report author corrects the report it is submitted for testing all over again, and this process continues until there are no errors found.
This process leads to numerous problems. First, if the metadata model is changed pursuant to the author's request, one or more previously created report specifications may fail to execute or produce incorrect results. Second, once a report specification is edited the previous version no longer exists and cannot be accessed for reference, or the previous version is stored in a content repository and is lost if the repository is lost or corrupted. Third, information stored in a repository cannot be accessed to convert an updated version having new errors back to a previous working version without errors. Last, there is no way to see the changes that have been made to the report specification, or to view recent outputs without manually running every report.
The present invention comprises a method and a system for applying continuous integration to business intelligence environments, including the business intelligence software installations, metadata models, report specifications, and analysis cubes, that overcomes the foregoing and other difficulties that have characterized the prior art. Continuous integration is a technique that has been practiced in the field of software development and is well known in the art. However, continuous integration has never been applied to business intelligence systems. The invention also employs version control, which is used to automatically record versions of business intelligence artifacts, including report specifications, metadata models, and analysis cubes.
The present invention applies continuous integration and automatic version control to improve the efficiency of developing, deploying, and altering business intelligence artifacts. In accordance with broader aspects of the invention, test cases can be defined for business intelligence artifacts. The test cases contain one or more assertions of characteristics that should be true of the business intelligence artifacts at a predetermined time. The assertions are used to verify that the business intelligence artifacts are working properly. There may be pre-execution assertions of characteristics that the business intelligence artifacts should possess before being executed, and post-execution assertions regarding characteristics of the outputs produced by execution of the business intelligence artifact. The test cases can be grouped into test suites and further into projects.
At a predetermined time interval, an automated agent executes any pre-execution assertions, executes the business intelligence artifact, stores the output of the execution of the business intelligence artifact and associates it with a version number, and executes any post-execution assertions. Any failures, or errors, are reported to the registered stakeholders, who are any persons with vested interests in the business intelligence artifacts, including business consumers, authors, etc. Thus, stakeholders are always assured that the system operates correctly and they are always aware of what has changed in the system.
By practicing the method of the present invention, business intelligence environments are better managed and more stable and predictable. Further, business intelligence artifact developers are able to better serve customers' ongoing needs by having the ability to update existing artifacts or revert to previously used artifacts without causing errors to or losing existing artifacts.